from sklearn_benchmarks.report import Reporting
import pandas as pd
pd.set_option('display.max_colwidth', None)
pd.set_option('display.max_columns', None)
pd.set_option('display.max_rows', None)
reporting = Reporting(config_file_path="config.yml")
reporting.run()
| hour | min | sec | |
|---|---|---|---|
| algo | |||
| KNeighborsClassifier | 0.0 | 35.0 | 48.872408 |
| daal4py_KNeighborsClassifier | 0.0 | 6.0 | 30.009766 |
| KNeighborsClassifier_kd_tree | 0.0 | 2.0 | 47.116247 |
| daal4py_KNeighborsClassifier_kd_tree | 0.0 | 0.0 | 32.654761 |
| KMeans_tall | 0.0 | 0.0 | 28.166049 |
| daal4py_KMeans_tall | 0.0 | 0.0 | 11.760189 |
| KMeans_short | 0.0 | 0.0 | 3.920000 |
| daal4py_KMeans_short | 0.0 | 0.0 | 2.106013 |
| LogisticRegression | 0.0 | 0.0 | 27.950741 |
| daal4py_LogisticRegression | 0.0 | 0.0 | 6.264059 |
| Ridge | 0.0 | 0.0 | 12.554886 |
| daal4py_Ridge | 0.0 | 0.0 | 2.720863 |
| HistGradientBoostingClassifier | 0.0 | 5.0 | 7.354109 |
| lightgbm | 0.0 | 5.0 | 1.804023 |
| xgboost | 0.0 | 5.0 | 49.684208 |
| catboost | 0.0 | 5.0 | 25.901929 |
| total | 1.0 | 8.0 | 38.959091 |
All estimators share the following hyperparameters:
| value | |
|---|---|
| algorithm | brute |
| estimator | function | n_samples_train | n_samples | n_features | mean_sklearn | stdev_sklearn | throughput | latency | n_jobs | n_neighbors | accuracy_score_sklearn | accuracy_score_daal4py | mean_daal4py | stdev_daal4py | speedup | stdev_speedup | sklearn_profiling | daal4py_profiling | |
|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|
| 0 | KNeighborsClassifier | fit | 1000000 | 1000000 | 100 | 0.426 | 0.000 | 1.877 | 0.000 | -1 | 5 | NaN | NaN | 0.603 | 0.000 | 0.707 | 0.000 | See | See |
| 1 | KNeighborsClassifier | predict | 1000000 | 1000 | 100 | 44.889 | 0.000 | 0.000 | 0.045 | -1 | 5 | 0.824 | 0.707 | 4.612 | 0.057 | 9.732 | 0.120 | See | See |
| 2 | KNeighborsClassifier | predict | 1000000 | 1 | 100 | 0.235 | 0.020 | 0.000 | 0.235 | -1 | 5 | 1.000 | 1.000 | 0.122 | 0.003 | 1.932 | 0.171 | See | See |
| 3 | KNeighborsClassifier | fit | 1000000 | 1000000 | 100 | 0.151 | 0.000 | 5.281 | 0.000 | -1 | 1 | NaN | NaN | 0.605 | 0.000 | 0.250 | 0.000 | See | See |
| 4 | KNeighborsClassifier | predict | 1000000 | 1000 | 100 | 35.341 | 0.000 | 0.000 | 0.035 | -1 | 1 | 0.735 | 0.940 | 4.680 | 0.049 | 7.552 | 0.080 | See | See |
| 5 | KNeighborsClassifier | predict | 1000000 | 1 | 100 | 0.230 | 0.018 | 0.000 | 0.230 | -1 | 1 | 1.000 | 1.000 | 0.122 | 0.004 | 1.888 | 0.158 | See | See |
| 6 | KNeighborsClassifier | fit | 1000000 | 1000000 | 100 | 0.171 | 0.000 | 4.679 | 0.000 | -1 | 100 | NaN | NaN | 0.593 | 0.000 | 0.288 | 0.000 | See | See |
| 7 | KNeighborsClassifier | predict | 1000000 | 1000 | 100 | 44.529 | 0.000 | 0.000 | 0.045 | -1 | 100 | 0.950 | 0.803 | 4.597 | 0.033 | 9.687 | 0.069 | See | See |
| 8 | KNeighborsClassifier | predict | 1000000 | 1 | 100 | 0.215 | 0.014 | 0.000 | 0.215 | -1 | 100 | 1.000 | 1.000 | 0.119 | 0.001 | 1.803 | 0.116 | See | See |
| 9 | KNeighborsClassifier | fit | 1000000 | 1000000 | 100 | 0.156 | 0.000 | 5.127 | 0.000 | 1 | 5 | NaN | NaN | 0.587 | 0.000 | 0.266 | 0.000 | See | See |
| 10 | KNeighborsClassifier | predict | 1000000 | 1000 | 100 | 28.855 | 0.120 | 0.000 | 0.029 | 1 | 5 | 0.824 | 0.803 | 4.611 | 0.050 | 6.258 | 0.073 | See | See |
| 11 | KNeighborsClassifier | predict | 1000000 | 1 | 100 | 0.264 | 0.009 | 0.000 | 0.264 | 1 | 5 | 1.000 | 1.000 | 0.120 | 0.003 | 2.192 | 0.089 | See | See |
| 12 | KNeighborsClassifier | fit | 1000000 | 1000000 | 100 | 0.150 | 0.000 | 5.338 | 0.000 | 1 | 100 | NaN | NaN | 0.600 | 0.000 | 0.250 | 0.000 | See | See |
| 13 | KNeighborsClassifier | predict | 1000000 | 1000 | 100 | 28.751 | 0.180 | 0.000 | 0.029 | 1 | 100 | 0.950 | 0.707 | 4.606 | 0.029 | 6.242 | 0.055 | See | See |
| 14 | KNeighborsClassifier | predict | 1000000 | 1 | 100 | 0.259 | 0.004 | 0.000 | 0.259 | 1 | 100 | 1.000 | 1.000 | 0.123 | 0.004 | 2.112 | 0.076 | See | See |
| 15 | KNeighborsClassifier | fit | 1000000 | 1000000 | 100 | 0.153 | 0.000 | 5.212 | 0.000 | 1 | 1 | NaN | NaN | 0.618 | 0.000 | 0.248 | 0.000 | See | See |
| 16 | KNeighborsClassifier | predict | 1000000 | 1000 | 100 | 18.784 | 0.097 | 0.000 | 0.019 | 1 | 1 | 0.735 | 0.940 | 4.678 | 0.027 | 4.015 | 0.031 | See | See |
| 17 | KNeighborsClassifier | predict | 1000000 | 1 | 100 | 0.249 | 0.003 | 0.000 | 0.249 | 1 | 1 | 1.000 | 1.000 | 0.121 | 0.002 | 2.062 | 0.042 | See | See |
| 18 | KNeighborsClassifier | fit | 1000000 | 1000000 | 2 | 0.059 | 0.000 | 0.272 | 0.000 | -1 | 5 | NaN | NaN | 0.108 | 0.000 | 0.544 | 0.000 | See | See |
| 19 | KNeighborsClassifier | predict | 1000000 | 1000 | 2 | 34.622 | 0.000 | 0.000 | 0.035 | -1 | 5 | 0.980 | 0.971 | 0.992 | 0.011 | 34.906 | 0.370 | See | See |
| 20 | KNeighborsClassifier | predict | 1000000 | 1 | 2 | 0.035 | 0.003 | 0.000 | 0.035 | -1 | 5 | 1.000 | 1.000 | 0.005 | 0.000 | 7.377 | 0.867 | See | See |
| 21 | KNeighborsClassifier | fit | 1000000 | 1000000 | 2 | 0.058 | 0.000 | 0.274 | 0.000 | -1 | 1 | NaN | NaN | 0.107 | 0.000 | 0.545 | 0.000 | See | See |
| 22 | KNeighborsClassifier | predict | 1000000 | 1000 | 2 | 27.451 | 0.162 | 0.000 | 0.027 | -1 | 1 | 0.973 | 0.986 | 1.087 | 0.015 | 25.253 | 0.384 | See | See |
| 23 | KNeighborsClassifier | predict | 1000000 | 1 | 2 | 0.021 | 0.001 | 0.000 | 0.021 | -1 | 1 | 1.000 | 1.000 | 0.005 | 0.000 | 4.242 | 0.437 | See | See |
| 24 | KNeighborsClassifier | fit | 1000000 | 1000000 | 2 | 0.062 | 0.000 | 0.259 | 0.000 | -1 | 100 | NaN | NaN | 0.108 | 0.000 | 0.571 | 0.000 | See | See |
| 25 | KNeighborsClassifier | predict | 1000000 | 1000 | 2 | 34.558 | 0.000 | 0.000 | 0.035 | -1 | 100 | 0.981 | 0.982 | 1.003 | 0.011 | 34.458 | 0.393 | See | See |
| 26 | KNeighborsClassifier | predict | 1000000 | 1 | 2 | 0.036 | 0.002 | 0.000 | 0.036 | -1 | 100 | 1.000 | 1.000 | 0.005 | 0.000 | 7.568 | 0.736 | See | See |
| 27 | KNeighborsClassifier | fit | 1000000 | 1000000 | 2 | 0.057 | 0.000 | 0.278 | 0.000 | 1 | 5 | NaN | NaN | 0.107 | 0.000 | 0.535 | 0.000 | See | See |
| 28 | KNeighborsClassifier | predict | 1000000 | 1000 | 2 | 19.738 | 0.113 | 0.000 | 0.020 | 1 | 5 | 0.980 | 0.982 | 0.996 | 0.007 | 19.814 | 0.176 | See | See |
| 29 | KNeighborsClassifier | predict | 1000000 | 1 | 2 | 0.030 | 0.002 | 0.000 | 0.030 | 1 | 5 | 1.000 | 1.000 | 0.005 | 0.000 | 6.304 | 0.520 | See | See |
| 30 | KNeighborsClassifier | fit | 1000000 | 1000000 | 2 | 0.061 | 0.000 | 0.264 | 0.000 | 1 | 100 | NaN | NaN | 0.108 | 0.000 | 0.559 | 0.000 | See | See |
| 31 | KNeighborsClassifier | predict | 1000000 | 1000 | 2 | 19.764 | 0.188 | 0.000 | 0.020 | 1 | 100 | 0.981 | 0.971 | 1.001 | 0.015 | 19.751 | 0.354 | See | See |
| 32 | KNeighborsClassifier | predict | 1000000 | 1 | 2 | 0.029 | 0.001 | 0.000 | 0.029 | 1 | 100 | 1.000 | 1.000 | 0.005 | 0.000 | 6.224 | 0.482 | See | See |
| 33 | KNeighborsClassifier | fit | 1000000 | 1000000 | 2 | 0.061 | 0.000 | 0.262 | 0.000 | 1 | 1 | NaN | NaN | 0.111 | 0.000 | 0.550 | 0.000 | See | See |
| 34 | KNeighborsClassifier | predict | 1000000 | 1000 | 2 | 12.577 | 0.068 | 0.000 | 0.013 | 1 | 1 | 0.973 | 0.986 | 1.103 | 0.023 | 11.397 | 0.249 | See | See |
| 35 | KNeighborsClassifier | predict | 1000000 | 1 | 2 | 0.015 | 0.001 | 0.000 | 0.015 | 1 | 1 | 1.000 | 1.000 | 0.005 | 0.001 | 2.780 | 0.430 | See | See |
All estimators share the following hyperparameters:
| value | |
|---|---|
| algorithm | kd_tree |
| estimator | function | n_samples_train | n_samples | n_features | mean_sklearn | stdev_sklearn | throughput | latency | n_jobs | n_neighbors | accuracy_score_sklearn | accuracy_score_daal4py | mean_daal4py | stdev_daal4py | speedup | stdev_speedup | sklearn_profiling | daal4py_profiling | |
|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|
| 0 | KNeighborsClassifier_kd_tree | fit | 1000000 | 1000000 | 10 | 3.103 | 0.000 | 0.026 | 0.000 | -1 | 1 | NaN | NaN | 0.794 | 0.000 | 3.909 | 0.000 | See | See |
| 1 | KNeighborsClassifier_kd_tree | predict | 1000000 | 1000 | 10 | 0.487 | 0.013 | 0.000 | 0.000 | -1 | 1 | 0.956 | 0.989 | 0.660 | 0.022 | 0.739 | 0.032 | See | See |
| 2 | KNeighborsClassifier_kd_tree | predict | 1000000 | 1 | 10 | 0.003 | 0.001 | 0.000 | 0.003 | -1 | 1 | 1.000 | 1.000 | 0.001 | 0.000 | 3.299 | 1.134 | See | See |
| 3 | KNeighborsClassifier_kd_tree | fit | 1000000 | 1000000 | 10 | 2.925 | 0.000 | 0.027 | 0.000 | 1 | 100 | NaN | NaN | 0.824 | 0.000 | 3.551 | 0.000 | See | See |
| 4 | KNeighborsClassifier_kd_tree | predict | 1000000 | 1000 | 10 | 4.855 | 0.043 | 0.000 | 0.005 | 1 | 100 | 0.980 | 0.971 | 0.116 | 0.005 | 41.795 | 1.764 | See | See |
| 5 | KNeighborsClassifier_kd_tree | predict | 1000000 | 1 | 10 | 0.003 | 0.001 | 0.000 | 0.003 | 1 | 100 | 1.000 | 1.000 | 0.000 | 0.000 | 7.719 | 4.014 | See | See |
| 6 | KNeighborsClassifier_kd_tree | fit | 1000000 | 1000000 | 10 | 2.918 | 0.000 | 0.027 | 0.000 | 1 | 1 | NaN | NaN | 0.807 | 0.000 | 3.615 | 0.000 | See | See |
| 7 | KNeighborsClassifier_kd_tree | predict | 1000000 | 1000 | 10 | 0.754 | 0.008 | 0.000 | 0.001 | 1 | 1 | 0.956 | 0.971 | 0.119 | 0.003 | 6.357 | 0.191 | See | See |
| 8 | KNeighborsClassifier_kd_tree | predict | 1000000 | 1 | 10 | 0.001 | 0.000 | 0.000 | 0.001 | 1 | 1 | 1.000 | 1.000 | 0.000 | 0.000 | 3.398 | 1.908 | See | See |
| 9 | KNeighborsClassifier_kd_tree | fit | 1000000 | 1000000 | 10 | 3.000 | 0.000 | 0.027 | 0.000 | -1 | 5 | NaN | NaN | 0.813 | 0.000 | 3.690 | 0.000 | See | See |
| 10 | KNeighborsClassifier_kd_tree | predict | 1000000 | 1000 | 10 | 0.884 | 0.016 | 0.000 | 0.001 | -1 | 5 | 0.974 | 0.989 | 0.661 | 0.022 | 1.338 | 0.051 | See | See |
| 11 | KNeighborsClassifier_kd_tree | predict | 1000000 | 1 | 10 | 0.004 | 0.001 | 0.000 | 0.004 | -1 | 5 | 1.000 | 1.000 | 0.001 | 0.000 | 3.685 | 1.304 | See | See |
| 12 | KNeighborsClassifier_kd_tree | fit | 1000000 | 1000000 | 10 | 3.176 | 0.000 | 0.025 | 0.000 | 1 | 5 | NaN | NaN | 0.777 | 0.000 | 4.087 | 0.000 | See | See |
| 13 | KNeighborsClassifier_kd_tree | predict | 1000000 | 1000 | 10 | 1.508 | 0.025 | 0.000 | 0.002 | 1 | 5 | 0.974 | 0.985 | 0.216 | 0.007 | 6.970 | 0.241 | See | See |
| 14 | KNeighborsClassifier_kd_tree | predict | 1000000 | 1 | 10 | 0.001 | 0.001 | 0.000 | 0.001 | 1 | 5 | 1.000 | 1.000 | 0.000 | 0.000 | 3.497 | 2.224 | See | See |
| 15 | KNeighborsClassifier_kd_tree | fit | 1000000 | 1000000 | 10 | 2.943 | 0.000 | 0.027 | 0.000 | -1 | 100 | NaN | NaN | 0.751 | 0.000 | 3.920 | 0.000 | See | See |
| 16 | KNeighborsClassifier_kd_tree | predict | 1000000 | 1000 | 10 | 3.006 | 0.042 | 0.000 | 0.003 | -1 | 100 | 0.980 | 0.985 | 0.220 | 0.010 | 13.656 | 0.641 | See | See |
| 17 | KNeighborsClassifier_kd_tree | predict | 1000000 | 1 | 10 | 0.006 | 0.001 | 0.000 | 0.006 | -1 | 100 | 1.000 | 1.000 | 0.000 | 0.000 | 14.973 | 7.423 | See | See |
| 18 | KNeighborsClassifier_kd_tree | fit | 1000000 | 1000000 | 2 | 0.875 | 0.000 | 0.018 | 0.000 | -1 | 1 | NaN | NaN | 0.547 | 0.000 | 1.598 | 0.000 | See | See |
| 19 | KNeighborsClassifier_kd_tree | predict | 1000000 | 1000 | 2 | 0.038 | 0.004 | 0.000 | 0.000 | -1 | 1 | 0.976 | 0.986 | 0.008 | 0.001 | 4.770 | 0.635 | See | See |
| 20 | KNeighborsClassifier_kd_tree | predict | 1000000 | 1 | 2 | 0.003 | 0.000 | 0.000 | 0.003 | -1 | 1 | 1.000 | 1.000 | 0.000 | 0.000 | 14.787 | 6.599 | See | See |
| 21 | KNeighborsClassifier_kd_tree | fit | 1000000 | 1000000 | 2 | 0.807 | 0.000 | 0.020 | 0.000 | 1 | 100 | NaN | NaN | 0.572 | 0.000 | 1.410 | 0.000 | See | See |
| 22 | KNeighborsClassifier_kd_tree | predict | 1000000 | 1000 | 2 | 0.057 | 0.005 | 0.000 | 0.000 | 1 | 100 | 0.985 | 0.975 | 0.001 | 0.000 | 62.704 | 17.748 | See | See |
| 23 | KNeighborsClassifier_kd_tree | predict | 1000000 | 1 | 2 | 0.001 | 0.000 | 0.000 | 0.001 | 1 | 100 | 1.000 | 1.000 | 0.000 | 0.000 | 4.702 | 3.491 | See | See |
| 24 | KNeighborsClassifier_kd_tree | fit | 1000000 | 1000000 | 2 | 0.818 | 0.000 | 0.020 | 0.000 | 1 | 1 | NaN | NaN | 0.543 | 0.000 | 1.508 | 0.000 | See | See |
| 25 | KNeighborsClassifier_kd_tree | predict | 1000000 | 1000 | 2 | 0.041 | 0.002 | 0.000 | 0.000 | 1 | 1 | 0.976 | 0.975 | 0.001 | 0.000 | 42.020 | 13.488 | See | See |
| 26 | KNeighborsClassifier_kd_tree | predict | 1000000 | 1 | 2 | 0.001 | 0.000 | 0.000 | 0.001 | 1 | 1 | 1.000 | 1.000 | 0.000 | 0.000 | 5.013 | 3.306 | See | See |
| 27 | KNeighborsClassifier_kd_tree | fit | 1000000 | 1000000 | 2 | 0.860 | 0.000 | 0.019 | 0.000 | -1 | 5 | NaN | NaN | 0.595 | 0.000 | 1.446 | 0.000 | See | See |
| 28 | KNeighborsClassifier_kd_tree | predict | 1000000 | 1000 | 2 | 0.038 | 0.002 | 0.000 | 0.000 | -1 | 5 | 0.986 | 0.986 | 0.008 | 0.001 | 4.610 | 0.753 | See | See |
| 29 | KNeighborsClassifier_kd_tree | predict | 1000000 | 1 | 2 | 0.003 | 0.000 | 0.000 | 0.003 | -1 | 5 | 1.000 | 1.000 | 0.000 | 0.000 | 13.690 | 8.864 | See | See |
| 30 | KNeighborsClassifier_kd_tree | fit | 1000000 | 1000000 | 2 | 0.811 | 0.000 | 0.020 | 0.000 | 1 | 5 | NaN | NaN | 0.550 | 0.000 | 1.475 | 0.000 | See | See |
| 31 | KNeighborsClassifier_kd_tree | predict | 1000000 | 1000 | 2 | 0.034 | 0.001 | 0.000 | 0.000 | 1 | 5 | 0.986 | 0.988 | 0.001 | 0.000 | 26.232 | 7.965 | See | See |
| 32 | KNeighborsClassifier_kd_tree | predict | 1000000 | 1 | 2 | 0.001 | 0.000 | 0.000 | 0.001 | 1 | 5 | 1.000 | 1.000 | 0.000 | 0.000 | 4.495 | 2.609 | See | See |
| 33 | KNeighborsClassifier_kd_tree | fit | 1000000 | 1000000 | 2 | 0.809 | 0.000 | 0.020 | 0.000 | -1 | 100 | NaN | NaN | 0.542 | 0.000 | 1.493 | 0.000 | See | See |
| 34 | KNeighborsClassifier_kd_tree | predict | 1000000 | 1000 | 2 | 0.062 | 0.003 | 0.000 | 0.000 | -1 | 100 | 0.985 | 0.988 | 0.001 | 0.000 | 46.863 | 12.264 | See | See |
| 35 | KNeighborsClassifier_kd_tree | predict | 1000000 | 1 | 2 | 0.003 | 0.001 | 0.000 | 0.003 | -1 | 100 | 1.000 | 1.000 | 0.000 | 0.000 | 17.742 | 7.972 | See | See |
All estimators share the following hyperparameters:
| value | |
|---|---|
| algorithm | full |
| n_clusters | 3 |
| max_iter | 30 |
| n_init | 1 |
| tol | 0.0 |
| estimator | function | n_samples_train | n_samples | n_features | n_iter_sklearn | mean_sklearn | stdev_sklearn | throughput | latency | init | adjusted_rand_score_sklearn | n_iter_daal4py | adjusted_rand_score_daal4py | mean_daal4py | stdev_daal4py | speedup | stdev_speedup | sklearn_profiling | daal4py_profiling | |
|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|
| 0 | KMeans_tall | fit | 1000000 | 1000000 | 2 | 30 | 0.712 | 0.000 | 0.674 | 0.000 | random | NaN | 30 | NaN | 0.382 | 0.0 | 1.865 | 0.000 | See | See |
| 1 | KMeans_tall | predict | 1000000 | 1000 | 2 | 30 | 0.002 | 0.000 | 0.252 | 0.000 | random | 0.001 | 30 | 0.001 | 0.000 | 0.0 | 8.276 | 4.066 | See | See |
| 2 | KMeans_tall | predict | 1000000 | 1 | 2 | 30 | 0.002 | 0.000 | 0.000 | 0.002 | random | 1.000 | 30 | 1.000 | 0.000 | 0.0 | 7.754 | 4.836 | See | See |
| 3 | KMeans_tall | fit | 1000000 | 1000000 | 2 | 30 | 0.679 | 0.000 | 0.707 | 0.000 | k-means++ | NaN | 30 | NaN | 0.340 | 0.0 | 1.999 | 0.000 | See | See |
| 4 | KMeans_tall | predict | 1000000 | 1000 | 2 | 30 | 0.002 | 0.000 | 0.271 | 0.000 | k-means++ | 0.001 | 30 | 0.001 | 0.000 | 0.0 | 7.090 | 3.421 | See | See |
| 5 | KMeans_tall | predict | 1000000 | 1 | 2 | 30 | 0.002 | 0.001 | 0.000 | 0.002 | k-means++ | 1.000 | 30 | 1.000 | 0.000 | 0.0 | 8.451 | 8.133 | See | See |
| 6 | KMeans_tall | fit | 1000000 | 1000000 | 100 | 30 | 8.522 | 0.000 | 2.816 | 0.000 | random | NaN | 30 | NaN | 4.654 | 0.0 | 1.831 | 0.000 | See | See |
| 7 | KMeans_tall | predict | 1000000 | 1000 | 100 | 30 | 0.002 | 0.000 | 11.978 | 0.000 | random | 0.002 | 30 | 0.002 | 0.000 | 0.0 | 5.653 | 1.735 | See | See |
| 8 | KMeans_tall | predict | 1000000 | 1 | 100 | 30 | 0.002 | 0.000 | 0.014 | 0.002 | random | 1.000 | 30 | 1.000 | 0.000 | 0.0 | 8.244 | 5.081 | See | See |
| 9 | KMeans_tall | fit | 1000000 | 1000000 | 100 | 30 | 8.616 | 0.000 | 2.786 | 0.000 | k-means++ | NaN | 30 | NaN | 4.383 | 0.0 | 1.966 | 0.000 | See | See |
| 10 | KMeans_tall | predict | 1000000 | 1000 | 100 | 30 | 0.002 | 0.000 | 11.935 | 0.000 | k-means++ | 0.002 | 30 | 0.002 | 0.000 | 0.0 | 5.716 | 2.838 | See | See |
| 11 | KMeans_tall | predict | 1000000 | 1 | 100 | 30 | 0.002 | 0.000 | 0.014 | 0.002 | k-means++ | 1.000 | 30 | 1.000 | 0.000 | 0.0 | 8.359 | 4.985 | See | See |
All estimators share the following hyperparameters:
| value | |
|---|---|
| algorithm | full |
| n_clusters | 300 |
| max_iter | 20 |
| n_init | 1 |
| tol | 0.0 |
| estimator | function | n_samples_train | n_samples | n_features | n_iter_sklearn | mean_sklearn | stdev_sklearn | throughput | latency | init | adjusted_rand_score_sklearn | n_iter_daal4py | adjusted_rand_score_daal4py | mean_daal4py | stdev_daal4py | speedup | stdev_speedup | sklearn_profiling | daal4py_profiling | |
|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|
| 0 | KMeans_short | fit | 10000 | 10000 | 2 | 20 | 0.343 | 0.0 | 0.009 | 0.000 | k-means++ | NaN | 20 | NaN | 0.058 | 0.0 | 5.916 | 0.000 | See | See |
| 1 | KMeans_short | predict | 10000 | 1000 | 2 | 20 | 0.002 | 0.0 | 0.143 | 0.000 | k-means++ | -0.001 | 20 | 0.002 | 0.001 | 0.0 | 3.032 | 0.447 | See | See |
| 2 | KMeans_short | predict | 10000 | 1 | 2 | 20 | 0.002 | 0.0 | 0.000 | 0.002 | k-means++ | 1.000 | 20 | 1.000 | 0.000 | 0.0 | 9.293 | 4.319 | See | See |
| 3 | KMeans_short | fit | 10000 | 10000 | 2 | 20 | 0.111 | 0.0 | 0.029 | 0.000 | random | NaN | 20 | NaN | 0.151 | 0.0 | 0.738 | 0.000 | See | See |
| 4 | KMeans_short | predict | 10000 | 1000 | 2 | 20 | 0.002 | 0.0 | 0.146 | 0.000 | random | -0.001 | 20 | 0.002 | 0.001 | 0.0 | 2.838 | 0.757 | See | See |
| 5 | KMeans_short | predict | 10000 | 1 | 2 | 20 | 0.002 | 0.0 | 0.000 | 0.002 | random | 1.000 | 20 | 1.000 | 0.000 | 0.0 | 8.217 | 4.563 | See | See |
| 6 | KMeans_short | fit | 10000 | 10000 | 100 | 20 | 1.240 | 0.0 | 0.129 | 0.000 | k-means++ | NaN | 20 | NaN | 0.286 | 0.0 | 4.338 | 0.000 | See | See |
| 7 | KMeans_short | predict | 10000 | 1000 | 100 | 20 | 0.003 | 0.0 | 4.595 | 0.000 | k-means++ | 0.305 | 20 | 0.333 | 0.002 | 0.0 | 1.992 | 0.265 | See | See |
| 8 | KMeans_short | predict | 10000 | 1 | 100 | 20 | 0.002 | 0.0 | 0.009 | 0.002 | k-means++ | 1.000 | 20 | 1.000 | 0.000 | 0.0 | 7.257 | 3.490 | See | See |
| 9 | KMeans_short | fit | 10000 | 10000 | 100 | 20 | 0.355 | 0.0 | 0.450 | 0.000 | random | NaN | 20 | NaN | 0.696 | 0.0 | 0.510 | 0.000 | See | See |
| 10 | KMeans_short | predict | 10000 | 1000 | 100 | 20 | 0.003 | 0.0 | 4.629 | 0.000 | random | 0.281 | 20 | 0.315 | 0.002 | 0.0 | 1.996 | 0.218 | See | See |
| 11 | KMeans_short | predict | 10000 | 1 | 100 | 20 | 0.002 | 0.0 | 0.009 | 0.002 | random | 1.000 | 20 | 1.000 | 0.000 | 0.0 | 7.591 | 2.965 | See | See |
All estimators share the following hyperparameters:
| value | |
|---|---|
| penalty | l2 |
| dual | False |
| tol | 0.0001 |
| C | 1.0 |
| fit_intercept | True |
| intercept_scaling | 1 |
| class_weight | NaN |
| random_state | NaN |
| solver | lbfgs |
| max_iter | 100 |
| multi_class | auto |
| verbose | 0 |
| warm_start | False |
| n_jobs | NaN |
| l1_ratio | NaN |
| estimator | function | n_samples_train | n_samples | n_features | n_iter | mean_sklearn | stdev_sklearn | throughput | latency | class_weight | l1_ratio | n_jobs | random_state | accuracy_score | mean_daal4py | stdev_daal4py | speedup | stdev_speedup | sklearn_profiling | daal4py_profiling | |
|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|
| 0 | LogisticRegression | fit | 1000000 | 1000000 | 100 | [20] | 17.117 | 0.0 | [-0.06892986] | 0.000 | NaN | NaN | NaN | NaN | NaN | 3.327 | 0.0 | 5.144 | 0.000 | See | See |
| 1 | LogisticRegression | predict | 1000000 | 1000 | 100 | [20] | 0.000 | 0.0 | [42.58797544] | 0.000 | NaN | NaN | NaN | NaN | 0.514 | 0.000 | 0.0 | 0.822 | 0.352 | See | See |
| 2 | LogisticRegression | predict | 1000000 | 1 | 100 | [20] | 0.000 | 0.0 | [0.13529397] | 0.000 | NaN | NaN | NaN | NaN | 0.000 | 0.000 | 0.0 | 0.426 | 0.360 | See | See |
| 3 | LogisticRegression | fit | 1000 | 1000 | 10000 | [26] | 1.413 | 0.0 | [1.47170724] | 0.001 | NaN | NaN | NaN | NaN | NaN | 1.194 | 0.0 | 1.184 | 0.000 | See | See |
| 4 | LogisticRegression | predict | 1000 | 100 | 10000 | [26] | 0.003 | 0.0 | [77.61047496] | 0.000 | NaN | NaN | NaN | NaN | 0.300 | 0.004 | 0.0 | 0.660 | 0.066 | See | See |
| 5 | LogisticRegression | predict | 1000 | 1 | 10000 | [26] | 0.000 | 0.0 | [15.3833638] | 0.000 | NaN | NaN | NaN | NaN | 0.000 | 0.001 | 0.0 | 0.140 | 0.088 | See | See |
All estimators share the following hyperparameters:
| value | |
|---|---|
| alpha | 1.0 |
| fit_intercept | True |
| normalize | deprecated |
| copy_X | True |
| max_iter | NaN |
| tol | 0.001 |
| solver | auto |
| random_state | NaN |
| estimator | function | n_samples_train | n_samples | n_features | n_iter | mean_sklearn | stdev_sklearn | throughput | latency | max_iter | random_state | r2_score | mean_daal4py | stdev_daal4py | speedup | stdev_speedup | sklearn_profiling | daal4py_profiling | |
|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|
| 0 | Ridge | fit | 1000 | 1000 | 10000 | NaN | 0.345 | 0.000 | 0.232 | 0.0 | NaN | NaN | NaN | 0.339 | 0.000 | 1.017 | 0.000 | See | See |
| 1 | Ridge | predict | 1000 | 1000 | 10000 | NaN | 0.013 | 0.001 | 5.984 | 0.0 | NaN | NaN | 0.12 | 0.023 | 0.001 | 0.588 | 0.048 | See | See |
| 2 | Ridge | predict | 1000 | 1 | 10000 | NaN | 0.000 | 0.000 | 0.659 | 0.0 | NaN | NaN | NaN | 0.000 | 0.000 | 0.575 | 0.354 | See | See |
| 3 | Ridge | fit | 1000000 | 1000000 | 100 | NaN | 1.698 | 0.000 | 0.471 | 0.0 | NaN | NaN | NaN | 0.443 | 0.000 | 3.834 | 0.000 | See | See |
| 4 | Ridge | predict | 1000000 | 1000 | 100 | NaN | 0.000 | 0.000 | 3.824 | 0.0 | NaN | NaN | 1.00 | 0.000 | 0.000 | 0.705 | 0.408 | See | See |
| 5 | Ridge | predict | 1000000 | 1 | 100 | NaN | 0.000 | 0.000 | 0.008 | 0.0 | NaN | NaN | NaN | 0.000 | 0.000 | 0.631 | 0.587 | See | See |
{
"system_info": {
"python": "3.8.10 | packaged by conda-forge | (default, May 11 2021, 07:01:05) [GCC 9.3.0]",
"executable": "/usr/share/miniconda/envs/sklbench/bin/python",
"machine": "Linux-5.4.0-1047-azure-x86_64-with-glibc2.10"
},
"dependencies_info": {
"pip": "21.1.2",
"setuptools": "49.6.0.post20210108",
"sklearn": "1.0.dev0",
"numpy": "1.20.3",
"scipy": "1.6.3",
"Cython": null,
"pandas": "1.2.4",
"matplotlib": "3.4.2",
"joblib": "1.0.1",
"threadpoolctl": "2.1.0"
},
"threadpool_info": [
{
"filepath": "/usr/share/miniconda/envs/sklbench/lib/libopenblasp-r0.3.15.so",
"prefix": "libopenblas",
"user_api": "blas",
"internal_api": "openblas",
"version": "0.3.15",
"num_threads": 2,
"threading_layer": "pthreads"
},
{
"filepath": "/usr/share/miniconda/envs/sklbench/lib/python3.8/site-packages/scikit_learn.libs/libgomp-f7e03b3e.so.1.0.0",
"prefix": "libgomp",
"user_api": "openmp",
"internal_api": "openmp",
"version": null,
"num_threads": 2
},
{
"filepath": "/usr/share/miniconda/envs/sklbench/lib/libgomp.so.1.0.0",
"prefix": "libgomp",
"user_api": "openmp",
"internal_api": "openmp",
"version": null,
"num_threads": 2
}
],
"cpu_count": 2
}